Advances in AAV capsid engineering: Integrating rational design, directed evolution and machine learning.

Journal: Molecular therapy : the journal of the American Society of Gene Therapy
PMID: 40176349

Abstract

Adeno-associated virus (AAV) has emerged as a highly promising vector for human gene therapy due to its favorable safety profile, versatility, and ability to transduce a wide range of tissues. However, natural AAV serotypes have shortcomings, including suboptimal transduction efficiency, pre-existing immunity, and a lack of tissue specificity, that hinder their therapeutic potential. To address these challenges, significant efforts are being applied to engineer novel AAV capsids. Rational design leverages structural insights to enhance capsid properties, directed evolution enables unbiased selection of superior variants, and machine learning accelerates discovery by computational analysis of high-throughput screening results to enable predictive algorithms. These strategies have yielded novel capsids with improved transduction efficiency, reduced immunogenicity, and enhanced tissue targeting. Future advances that continue to integrate such multi-disciplinary approaches will further drive the clinical translation of AAV-based therapies.

Authors

  • Alan M Nisanov
    Department of Chemistry, University of California, Berkeley, Berkeley CA 94720, USA.
  • Julio A Rivera de Jesús
    Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA; Graduate Program in Bioengineering, University of California, Berkeley, San Francisco and University of California, Berkeley, CA 94720, USA; Department of Neurological Surgery, University of California, San Francisco, CA 94143, USA.
  • David V Schaffer
    Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA. Electronic address: schaffer@berkeley.edu.

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